Two-sided matching markets rely on preferences from both sides, yet it is often impractical to evaluate preferences. Participants, therefore, conduct a limited number of interviews, which provide early, noisy impressions and shape final decisions. We study bandit learning in matching markets with interviews, modeling interviews as \textit{low-cost hints} that reveal partial preference information to both sides. Our framework departs from existing work by allowing firm-side uncertainty: firms, like agents, may be unsure of their own preferences and can make early hiring mistakes by hiring less preferred agents. To handle this, we extend the firm's action space to allow \emph{strategic deferral} (choosing not to hire in a round), enabling recovery from suboptimal hires and supporting decentralized learning without coordination. We design novel algorithms for (i) a centralized setting with an omniscient interview allocator and (ii) decentralized settings with two types of firm-side feedback. Across all settings, our algorithms achieve time-independent regret, a substantial improvement over the $O(\log T)$ regret bounds known for learning stable matchings without interviews. Also, under mild structured markets, decentralized performance matches the centralized counterpart up to polynomial factors in the number of agents and firms.

翻译：双边匹配市场依赖于双方的偏好，但评估偏好往往不切实际。因此，参与者会进行有限次数的面试，这些面试提供了早期、带有噪声的印象，并塑造了最终决策。我们研究带面试的匹配市场中的赌博机学习，将面试建模为向双方揭示部分偏好信息的*低成本提示*。我们的框架与现有工作的不同之处在于允许企业方的不确定性：企业像代理一样，可能不确定自身的偏好，并可能通过雇佣偏好较低的代理而犯下早期雇佣错误。为处理此问题，我们扩展了企业的行动空间，允许*战略性延迟*（选择在某一轮不雇佣），从而能够从次优雇佣中恢复，并支持无需协调的去中心化学习。我们为以下两种场景设计了新颖算法：(i) 具有全知面试分配者的中心化场景，以及(ii) 具有两种企业方反馈类型的去中心化场景。在所有场景中，我们的算法实现了与时间无关的遗憾，这相对于已知的无面试学习稳定匹配的$O(\log T)$遗憾界有显著改进。此外，在温和的结构化市场条件下，去中心化性能在代理和企业数量上的多项式因子范围内与中心化对应方案相匹配。